Sufriadi Elly, Idroes Rinaldi, Meilina Hesti, Munawar Agus Arip, Indrayanto Gunawan
Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia.
Department of Chemistry, Faculty of Mathematics and Natural Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia.
ACS Omega. 2023 Mar 22;8(13):12348-12361. doi: 10.1021/acsomega.3c00080. eCollection 2023 Apr 4.
This study aims to test chemometrically partial least squares-discriminant analysis (PLS-DA) classification models to detect the adulteration of patchouli oil (PO) with gurjun balsam oil (GBO) by utilization of Fourier transform infrared spectroscopy. Unsupervised analysis was tested using the pattern recognition method using the principal component analysis model against the original spectrum at wavenumbers 4000-500 cm and at the fingerprint area (1800-600 cm). Model testing was also carried out on the spectrum that had been pre-processed using the standard normal variate, second derivative Savitzky-Golay, and normalization approaches. Variable Y samples used were certified reference material (CRM), PO, GBO, and PO forged with GBO (PGBO) with a counterfeiting ratio of 0.5 (v/v) to 10% (v/v) with an interval of 0.5%. The same treatment was carried out on testing of the PLS-DA model. In pattern recognition tests, the best separation of the original spectrum was obtained at wavenumbers 1800-600 cm. The model was further tested on PLS-DA by making assumptions or codes for CRM, PO, GBO, and PGBO as +2, +1, 0, and -1, respectively. The results of the model analysis showed that even at the lowest counterfeiting ratio (0.5%), the presence of counterfeiting material was detected by the PLS-DA model. The RMSEC value is close to zero with a value of 0.22, and the R square is close to 1, which is 0.954. This very significant separation is clearly illustrated in the loading plot and bi-plot due to the contribution of chemical compounds in the GBO that undergo vibration at wavenumbers 603, 786, and 1386 cm. Validation of the PLS-DA model was carried out strongly using the PLS model, and it showed that the difference between the calibration concentration and the prediction was very low (average 0.45) with an accuracy percent above 99%. The efficacy of the model is further substantiated by the consistent and precise values of sensitivity and selectivity, obtained from both the training set and test set.
本研究旨在通过利用傅里叶变换红外光谱法，以化学计量学方式测试偏最小二乘判别分析（PLS-DA）分类模型，以检测广藿香油（PO）中掺入古芸香脂油（GBO）的情况。使用主成分分析模型的模式识别方法，在波数4000 - 500 cm以及指纹区（1800 - 600 cm）对原始光谱进行无监督分析测试。还对使用标准正态变量、二阶导数Savitzky-Golay和归一化方法预处理后的光谱进行模型测试。所使用的变量Y样本为有证标准物质（CRM）、PO、GBO以及掺入GBO的PO（PGBO），掺假比例为0.5%（v/v）至10%（v/v），间隔为0.5%。对PLS-DA模型的测试也进行了同样的处理。在模式识别测试中，在波数1800 - 600 cm处获得了原始光谱的最佳分离效果。通过分别将CRM、PO、GBO和PGBO设定为 +2、+1、0和 -1的假设或编码，对PLS-DA模型进行进一步测试。模型分析结果表明，即使在最低掺假比例（0.5%）时，PLS-DA模型也能检测到掺假物质的存在。RMSEC值接近零，为0.22，R平方接近1，为0.954。由于GBO中在波数603、786和1386 cm处发生振动的化合物的贡献，这种非常显著的分离在载荷图和双标图中得到了清晰的体现。使用PLS模型对PLS-DA模型进行了有力的验证，结果表明校准浓度与预测值之间的差异非常小（平均0.45），准确率超过99%。从训练集和测试集获得的灵敏度和选择性的一致且精确的值进一步证实了该模型的有效性。
